Quantum Computing

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Quantum computing uses quantum mechanical effects to perform calculations that are far beyond the capabilities of conventional supercomputers. It therefore holds the potential to efficiently deal with previously unsolvable problems in the future. With the two key aspects quantum-based machine learning and quantum optimization, the group studies industry-relevant applications and methods so that the transfer of quantum computing on a broad industrial scale can take place at an early stage. The focus is further developing new quantum algorithms and refining existing ones, taking the current progress of the quantum computing hardware into account. Despite this promising outlook, some challenges can still only be overcome in the mid-term. One of the main tasks is to identify which applications can benefit from today’s quantum computers.


Key application areas at Fraunhofer IPA include manufacturing engineering and automation, as well as hydrogen technology. These are closely related to the need for sustainable development, which can trigger transformation processes that are relevant to the future.

As part of the Fraunhofer “Quantum Computing” competence network, Fraunhofer IPA has exclusive access to IBM Q System One, Europe's first commercial quantum computer, which went into operation in Ehningen near Stuttgart in 2021.


Quantum Machine Learning

Quantum Machine Learning (QML) uses properties of quantum physics to solve challenges relating to machine learning and artificial intelligence. Our work aims to push the limits of the current state of the art in QML and to find new ways of using quantum computers in this field.


Quantum Optimization

Complex optimization problems are encountered in many branches of industry. An efficient solution has a direct impact on key aspects such as profit, material consumption and sustainability. We are studying how quantum computing can be used to do this.


Application areas of quantum computing

As a potentially disruptive technology, quantum computing can affect progress in key applications. Two of the application areas that Fraunhofer IPA focuses on in particular are manufacturing and hydrogen technology. 



Automated machine learning (AutoML) enables low-threshold access to AI solutions. The “AutoQML” project extends this approach with quantum computing-based methods so that it can be transferred to industry at an early stage.



In the SEQUOIA project, software for industrial hybrid quantum applications and algorithms is being engineered. In the project, new methods, tools, and procedures for quantum computing are researched, developed, and tested with a view to making them suitable for industrial use in the future.



The “Degrad-EL3-Q” project focuses on finding ways to use quantum computers in order to analyze the lifetime of electrolyzers. It forms part of the lead project “H2Giga”, in which the serial production of electrolyzers is being developed.



AQUAS aims to make the catalytic processes of electrolytic materials accessible through quantum simulations. Fraunhofer IPA is conducting research on quantum-based AI methods to complement these simulations.



We have developed sQUlearn, a user-friendly, NISQ-ready Python library for quantum machine learning, designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both quantum machine learning researchers and practitioners.